Monday, May 7, 2012

Handwriting recognizer using neural network

Our project implements a touchpad input system which takes user input and converts it to a printed character. Currently, the device only recognizes the 26 letters of the alphabet, but our training system could be easily generalized to include any figure of completely arbitrary shape, including alphanumerics, punctuation, and other symbols. A stylus is used to draw the figure/character on the touchpad, and the result is shown on an LCD display. Pushbutton controls allow the user to format the text on the display.
We chose this project because touchscreens and touchpads are prevalent today in many new technologies, especially with the recent popularity of smartphones and tablet PCs. We wanted to explore the capabilities of such a system and were further intrigued by our research into different letter-recognition methods. Finally, we have had previous course experience in signal processing, computer vision, and artificial intelligence; we feel that this project was an excellent way to synthesize all of this knowledge.